Object-centric representations. have been proposed in many forms at the intersection of computer vision and robotics. For example, object-centric scene graphs can be constructed for integrated task and motion planning [1]}, [2]}, [3]}, navigation [4]}, [5]}, relational inference [6]}, [7]}, dynamics modeling [8]}, [9]}, [10]}, [11]}, [12]}, model predictive control [13]}, [14]}, [15]} or visual imitation learning [16]}. Similar to our work, [16]} propose to abstract video demonstrations as object-centric graphs for the problem of single-video cross-embodiment imitation, and act by minimizing the difference between the demonstration graph and a graph constructed from observations captured at each step. As such, their method is limited to same-domain visual trajectory following, whereas we learn a general alignment function for cross-domain cross-embodiment imitation and leverage Interaction Networks [8]} for modeling graph-abstracted spatial interactions rather than relying on heuristics.
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